Transcription And Notation With Pytheory

Transcription & Notation

Getting music into PyTheory from audio/MIDI, and out to MIDI, sheet music, and tab.

Transcribe a recording → notes / MIDI

from pytheory import Score

score = Score.from_wav("hum.wav", bpm=80)        # estimates tempo if bpm omitted
for name, part in score.parts.items():
    print(name, len(part.notes), "notes")
score.save_midi("hum.mid")

• Score.from_wav(path, *, bpm=None, quantize=None, split=False, fmin=50, fmax=1500).

quantize=0.25 snaps to sixteenths; split=True separates a full mix into bass + melody (and drums) instead of one monophonic melody part.

• .m4a/.mp3 work if afconvert/ffmpeg is available; WAV always works.
• CLI equivalent: pytheory transcribe hum.m4a out.mid (add --split,

--quantize 0.25, --bpm 90).

Identify the chord in an audio buffer

from pytheory.audio import identify_chord
import scipy.io.wavfile
sr, data = scipy.io.wavfile.read("clip.wav")
identify_chord(data, sr)
# {'symbol': 'D7', 'confidence': 0.76, 'notes': ['D', 'F#', 'A', 'C']}  (or None)

Returns a best-guess symbol with a confidence (0..1) and the detected notes, or None if it can't tell. Works best on clean, sustained chords; it's a real-time recognizer, not a perfect oracle. (The live version is pytheory tune --chords, in the guitar skill.)

Import MIDI

from pytheory import Score
score = Score.from_midi("song.mid")

Export to every format

score.save_midi("song.mid")                                  # MIDI (drums ch 10)
open("song.abc", "w").write(score.to_abc(title="Song", key="C"))
open("song.xml", "w").write(score.to_musicxml(title="Song"))   # MusicXML for notation apps
open("song.ly",  "w").write(score.to_lilypond(title="Song", key="C"))
print(score.to_tab("part_name"))                             # ASCII guitar tab for a part

• to_tab(part_name, tuning="guitar", frets=24) turns a single part into tab.
• to_musicxml opens in MuseScore/Finale/Sibelius; to_lilypond engraves to PDF

via LilyPond; to_abc is compact plain-text notation.

Lead sheets (chord symbols + fret diagrams)

to_lilypond can render a chord part as a lead sheet — chord names, fret diagrams, and/or tab above the melody staff:

ly = score.to_lilypond(chord_names=True, fretboards=True, tab=True)
# chord_names -> a ChordNames row (C  G  Am  F)
# fretboards  -> a FretBoards row using PyTheory's OWN voicings (not LilyPond's)
# tab         -> a TabStaff of the progression
# chord_part="comp" picks which part supplies the harmony (else the first
#   chord-bearing part); fretboard=Fretboard.guitar(...) sets the diagram source

The fret diagrams come straight from PyTheory's Fretboard, so they match score.to_tab() / what it would actually play. Compile with lilypond leadsheet.ly → PDF.

A complete round-trip

from pytheory import Score, Key
score = Score.from_wav("melody.wav", quantize=0.25)          # hum -> notes
key = Key.detect(*[n.tone.name for n in score.parts["melody"].notes if n.tone])
score.save_midi("melody.mid")                                # -> DAW
open("melody.xml", "w").write(score.to_musicxml(title="My Melody"))   # -> sheet music
print("Detected key:", key)

Tips

• Transcription is monophonic by default — one note at a time. Use split=True

for full mixes.

• Pass bpm= if you know the tempo; otherwise it's estimated and timing/quantize

is interpreted against that estimate.

• identify_chord returns a dict (or None) — check confidence before trusting

the symbol.

• NumPy/SciPy ship as PyTheory dependencies, so scipy.io.wavfile (for reading

the audio buffer) needs no extra install.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kennethreitz
• Source: kennethreitz/pytheory-skill
• License: MIT

Install and usage instructions live in the source repository linked above.